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Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

Sila AM, Shepherd KD, Pokhariyal GP - Chemometr Intell Lab Syst (2016)

Bottom Line: The root mean square error of prediction was computed using a one-third-holdout validation set.In summary, the results show that global models outperformed the subspace models.We, therefore, conclude that global models are more accurate than the local models except in few cases.

View Article: PubMed Central - PubMed

Affiliation: World Agroforestry Centre (ICRAF), P.O. Box 30677-00100 GPO, Nairobi, Kenya; School of Mathematics, University of Nairobi, P.O Box 30196-00100 GPO, Nairobi, Kenya.

ABSTRACT

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

No MeSH data available.


Related in: MedlinePlus

Archetype-SS (left plot) and SOM-SS (right plot) confidence interval plot showing mean soil total carbon (%) differences among the spectral subspaces. All the four subspaces in each type are significantly different.
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f0020: Archetype-SS (left plot) and SOM-SS (right plot) confidence interval plot showing mean soil total carbon (%) differences among the spectral subspaces. All the four subspaces in each type are significantly different.

Mentions: We used HQISS to understand the common spectral features within a subspace. We averaged all spectra in each subspace and obtained a representative spectrum with different shapes and intensities Fig. 4. Some of the clay minerals found in soil include kaolinite, Halloysite, quartz, carbonate, gibbsite, Illite, and smectite in widely varying proportions [24]. Illite minerals are characterized by a broad and poorly defined hydroxyl stretching band near 3620 and 3630 cm− 1[24]. Illite rich soils are also referred to as desert loam soils and from spectral subspaces obtained they are dominant with about 80% of the samples grouped to be similar to Illite.


Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

Sila AM, Shepherd KD, Pokhariyal GP - Chemometr Intell Lab Syst (2016)

Archetype-SS (left plot) and SOM-SS (right plot) confidence interval plot showing mean soil total carbon (%) differences among the spectral subspaces. All the four subspaces in each type are significantly different.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834557&req=5

f0020: Archetype-SS (left plot) and SOM-SS (right plot) confidence interval plot showing mean soil total carbon (%) differences among the spectral subspaces. All the four subspaces in each type are significantly different.
Mentions: We used HQISS to understand the common spectral features within a subspace. We averaged all spectra in each subspace and obtained a representative spectrum with different shapes and intensities Fig. 4. Some of the clay minerals found in soil include kaolinite, Halloysite, quartz, carbonate, gibbsite, Illite, and smectite in widely varying proportions [24]. Illite minerals are characterized by a broad and poorly defined hydroxyl stretching band near 3620 and 3630 cm− 1[24]. Illite rich soils are also referred to as desert loam soils and from spectral subspaces obtained they are dominant with about 80% of the samples grouped to be similar to Illite.

Bottom Line: The root mean square error of prediction was computed using a one-third-holdout validation set.In summary, the results show that global models outperformed the subspace models.We, therefore, conclude that global models are more accurate than the local models except in few cases.

View Article: PubMed Central - PubMed

Affiliation: World Agroforestry Centre (ICRAF), P.O. Box 30677-00100 GPO, Nairobi, Kenya; School of Mathematics, University of Nairobi, P.O Box 30196-00100 GPO, Nairobi, Kenya.

ABSTRACT

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

No MeSH data available.


Related in: MedlinePlus